Free Databricks Practice Questions: 25 Sample Questions With Answers

Why Practice Questions Matter for Databricks Exam Prep

If you're preparing for the Databricks Certified Data Engineer Associate exam, you already know that reading documentation alone won't cut it. The exam is scenario-driven, testing not just whether you know a concept but whether you can apply it in realistic data engineering situations. That's why a solid databricks certified data engineer associate practice test routine is one of the highest-leverage activities you can invest in before exam day.

This article gives you 25 free sample questions spanning all five exam domains, complete with detailed answer explanations. These aren't throwaway trivia questions - each one mirrors the style, depth, and ambiguity you'll encounter on the real databricks practice exam. Work through them honestly, check your answers, and use the domain breakdown section to identify where you need more study time.

Before diving into the questions, let's quickly review the exam structure so you know how each question maps to what Databricks actually tests. If you want a deeper dive into study strategy, our Databricks Data Engineer Associate Study Guide 2026 (Updated July 2025 Exam) covers the full curriculum in detail.

Exam Overview: What You Need to Know Before You Start

The Databricks Certified Data Engineer Associate exam (updated July 2025) is administered by Databricks and costs $200. You have 90 minutes to answer 45 multiple-choice questions, and you need a score of at least 70% - meaning you must answer roughly 32 questions correctly to pass. The certification is valid for two years.

The exam covers five domains with different weightings:

Domain	Topic	Weight
Domain 1	Databricks Intelligence Platform	10%
Domain 2	Development and Ingestion	30%
Domain 3	Data Processing and Transformations	30%
Domain 4	Productionizing Data Pipelines	20%
Domain 5	Data Governance and Quality	10%

Domains 2 and 3 together account for 60% of the exam, so Apache Spark, Delta Lake, and transformation logic deserve the bulk of your preparation time. For a detailed look at how hard this exam really is, check out our article on Is the Databricks Certification Exam Hard? Real Pass Rates and Difficulty.

25 Free Databricks Practice Questions With Answers

Domain 1: Databricks Intelligence Platform (Questions 1-3)

Question 1. Which component of the Databricks Intelligence Platform is responsible for storing and managing metadata for tables, views, and storage credentials across multiple workspaces?

• A) Delta Lake
• B) Unity Catalog
• C) Databricks Repos
• D) Hive Metastore

Answer: B - Unity Catalog. Unity Catalog is Databricks' unified governance solution that manages metadata, access control, and lineage across all workspaces in an account. The legacy Hive Metastore is workspace-scoped and does not span multiple workspaces.

Question 2. A data team wants to share a live, read-only version of a Delta table with a partner organization without copying any data. Which Databricks feature best supports this?

• A) Delta Sharing
• B) DEEP CLONE
• C) External Tables
• D) Databricks Marketplace

Answer: A - Delta Sharing. Delta Sharing is an open protocol that lets you share live Delta tables with external recipients without duplicating data. DEEP CLONE copies data; external tables still require the recipient to have access to the underlying storage.

Question 3. In a Databricks workspace, what is the primary purpose of a cluster policy?

• A) To restrict network egress from a cluster
• B) To enforce configuration rules and cost controls on cluster creation
• C) To define the execution order of notebook cells
• D) To schedule recurring cluster restarts

Answer: B. Cluster policies let administrators define allowed configuration ranges (instance types, autoscaling limits, tags) so that users can't spin up unnecessarily large or expensive clusters.

Domain 2: Development and Ingestion (Questions 4-11)

Question 4. You use Auto Loader to ingest files from cloud storage. Which format does Auto Loader use by default to track which files have already been processed?

• A) A Delta table checkpoint
• B) A RocksDB state store
• C) A cloud object listing checkpoint directory
• D) A Hive Metastore partition

Answer: C. Auto Loader uses a checkpoint directory in cloud storage to track file state. In file notification mode, it also uses cloud notification services, but the checkpoint directory is the default state storage mechanism.

Question 5. Which PySpark method is used to read a streaming source with Auto Loader?

• A) spark.read.format("cloudFiles")
• B) spark.readStream.format("cloudFiles")
• C) spark.readStream.format("autoloader")
• D) spark.read.format("delta").stream()

Answer: B. Auto Loader is invoked via spark.readStream.format("cloudFiles"). The readStream call is required because Auto Loader processes data incrementally as a streaming source.

Question 6. A Delta table has accumulated many small files after thousands of incremental inserts. Which operation will compact these files and improve query performance?

• A) VACUUM
• B) OPTIMIZE
• C) ANALYZE
• D) ZORDER BY

Answer: B - OPTIMIZE. OPTIMIZE compacts small files into larger Parquet files. ZORDER BY is a clause used with OPTIMIZE to co-locate related data, but it is not itself a standalone command. VACUUM removes old file versions; it doesn't compact.

Question 7. What is the minimum retention period enforced by VACUUM by default in Delta Lake?

• A) 24 hours
• B) 7 days
• C) 30 days
• D) 90 days

Answer: B - 7 days. Delta Lake's default retention threshold for VACUUM is 168 hours (7 days). Running VACUUM with a shorter period requires explicitly overriding the safety check.

Question 8. You write a notebook that uses MERGE INTO to upsert records into a Delta table. Which of the following is a valid use case for the WHEN NOT MATCHED clause?

• A) Updating existing records that match a join condition
• B) Deleting source records that have no match in the target
• C) Inserting new records from the source that don't exist in the target
• D) Rolling back failed merge operations

Answer: C. WHEN NOT MATCHED handles rows in the source that have no corresponding row in the target - typically used for inserts. WHEN MATCHED handles updates and deletes for existing rows.

Question 9. Which table property enables Delta Lake to automatically optimize file size during write operations without running OPTIMIZE manually?

• A) delta.autoOptimize.optimizeWrite
• B) delta.enableAutoCompact
• C) delta.targetFileSize
• D) spark.databricks.delta.autoOptimize

Answer: A. Setting delta.autoOptimize.optimizeWrite = true enables Optimized Writes, which uses shuffle to produce fewer, larger files at write time. Auto Compact (delta.autoOptimize.autoCompact) is a related but separate feature that compacts after writes.

Question 10. A data engineer needs to query a Delta table as it existed three days ago for audit purposes. Which Delta Lake feature supports this?

• A) Delta Cloning
• B) Time Travel
• C) Change Data Feed
• D) Delta Sharing

Answer: B - Time Travel. Delta Lake's time travel allows querying historical versions of a table using VERSION AS OF or TIMESTAMP AS OF syntax, as long as the files haven't been vacuumed.

Question 11. When using Databricks Repos, which of the following Git operations is NOT directly supported within the Databricks UI?

• A) Commit changes
• B) Create a new branch
• C) Interactive rebase
• D) Pull latest changes

Answer: C - Interactive rebase. Databricks Repos supports common Git operations like commit, push, pull, branch creation, and merging. Interactive rebase is a complex Git operation that must be performed outside the Databricks UI using a local Git client.

Domain 3: Data Processing and Transformations (Questions 12-19)

Question 12. Which Spark action triggers the execution of a lazy transformation chain?

• A) .filter()
• B) .select()
• C) .count()
• D) .withColumn()

Answer: C - .count(). In Spark, transformations like filter, select, and withColumn are lazy - they define a computation plan but don't execute it. Actions like count, show, collect, and write trigger actual execution.

Question 13. What is the result of performing a broadcast join in Spark when one DataFrame is very small?

• A) Spark repartitions both DataFrames to the same number of partitions
• B) Spark sends the smaller DataFrame to all worker nodes, eliminating a shuffle
• C) Spark caches the smaller DataFrame in the driver memory
• D) Spark converts the join to a cross join automatically

Answer: B. Broadcast joins replicate the smaller DataFrame to every executor, avoiding the expensive shuffle that a standard sort-merge join requires. This is triggered automatically when the table is below spark.sql.autoBroadcastJoinThreshold or explicitly with the broadcast() hint.

Question 14. You have a DataFrame with duplicate rows. Which method removes ALL duplicates based on ALL columns?

• A) .drop_duplicates(subset=["id"])
• B) .distinct()
• C) .dropDuplicates()
• D) Both B and C

Answer: D - Both B and C. In PySpark, .distinct() and .dropDuplicates() (with no arguments) are functionally equivalent and both remove rows where all column values are identical. .dropDuplicates(subset=...) allows column-specific deduplication.

Question 15. Which window function returns the rank of a row within a partition, with gaps in ranking for tied values?

• A) dense_rank()
• B) row_number()
• C) rank()
• D) ntile()

Answer: C - rank(). rank() assigns the same rank to tied rows but skips subsequent ranks (e.g., 1, 1, 3). dense_rank() doesn't skip ranks (1, 1, 2). row_number() assigns unique sequential numbers regardless of ties.

Question 16. A pipeline reads from a Delta table and needs to process only rows that have changed since the last run. Which Delta Lake feature is most appropriate?

• A) Time Travel with VERSION AS OF
• B) Delta Change Data Feed (CDF)
• C) MERGE INTO with WHEN NOT MATCHED
• D) Auto Loader with schema evolution

Answer: B - Delta Change Data Feed. CDF captures row-level changes (inserts, updates, deletes) in a Delta table, making it ideal for incremental downstream processing. It must be enabled on the table with delta.enableChangeDataFeed = true.

Question 17. What does the spark.sql.shuffle.partitions configuration control?

• A) The number of partitions when reading from cloud storage
• B) The number of partitions produced after a shuffle operation like a join or aggregate
• C) The maximum partition size for Delta writes
• D) The number of CPU cores allocated per executor

Answer: B. spark.sql.shuffle.partitions (default: 200) controls how many partitions are created after wide transformations that require a shuffle. For small datasets, reducing this value can significantly improve performance.

Question 18. You apply .cache() to a DataFrame but notice the second action is not faster than the first. What is the most likely cause?

• A) The DataFrame was written to disk before caching
• B) .cache() is a transformation, so the data isn't cached until the first action
• C) Spark automatically uncaches DataFrames after each action
• D) Caching only works with RDDs, not DataFrames

Answer: B. .cache() is lazy - it marks the DataFrame for caching but doesn't populate the cache until the first action executes and materializes the data. The second action benefits from the cache, not the first two.

Question 19. Which SQL function in Databricks returns the current timestamp at query execution time and is non-deterministic?

• A) now()
• B) current_timestamp()
• C) date_format()
• D) to_timestamp()

Answer: B - current_timestamp(). This function returns the current timestamp when called. It is non-deterministic, meaning repeated calls within the same query can produce different values, which matters for reproducibility in pipeline design.

Domain 4: Productionizing Data Pipelines (Questions 20-22)

Question 20. In Databricks Delta Live Tables (DLT), what is the difference between a LIVE TABLE and a STREAMING LIVE TABLE?

• A) LIVE TABLEs process all data on every pipeline run; STREAMING LIVE TABLEs process only new records since the last run
• B) LIVE TABLEs are stored externally; STREAMING LIVE TABLEs are stored in Delta format
• C) LIVE TABLEs support Python only; STREAMING LIVE TABLEs support SQL only
• D) There is no functional difference; they are aliases

Answer: A. LIVE TABLEs (materialized views) recompute from all source data each run. STREAMING LIVE TABLEs process only new or changed data incrementally, making them far more efficient for large, continuously growing datasets.

Question 21. A Databricks Job is configured with a retry policy of 3 retries. The job fails on the first attempt due to an out-of-memory error. What happens next?

• A) The job immediately marks as failed without retrying
• B) Databricks retries the job up to 3 more times
• C) Databricks automatically increases cluster memory and retries once
• D) The job enters a paused state waiting for manual intervention

Answer: B. With a retry count of 3, Databricks will attempt to rerun the failed task up to 3 additional times. It does not automatically adjust cluster resources - that requires manual reconfiguration or job policy changes.

Question 22. Which trigger type in Structured Streaming processes all available data as a one-time micro-batch and then stops the stream?

• A) trigger(processingTime="0 seconds")
• B) trigger(once=True)
• C) trigger(continuous="1 second")
• D) trigger(availableNow=True)

Answer: D - trigger(availableNow=True). While trigger(once=True) processes data in a single micro-batch, trigger(availableNow=True) (introduced in Spark 3.3) processes all available data in multiple micro-batches and then stops, offering better parallelism and efficiency. Both are tested on the exam.

Domain 5: Data Governance and Quality (Questions 23-25)

Question 23. In Unity Catalog, what is the three-level namespace hierarchy for referencing a table?

• A) Workspace → Database → Table
• B) Catalog → Schema → Table
• C) Metastore → Catalog → View
• D) Account → Workspace → Table

Answer: B - Catalog → Schema → Table. Unity Catalog introduces a three-level namespace: catalog.schema.table. The legacy Hive Metastore used a two-level namespace (database.table), which is a key difference candidates must know.

Question 24. A data engineer wants to prevent users from accessing columns containing PII in a Unity Catalog table without completely hiding the table. Which feature should they use?

• A) Row filters
• B) Column masks
• C) Table ACLs
• D) Dynamic views

Answer: B - Column masks. Column masks in Unity Catalog apply a masking function to specific columns based on the user's identity or group, allowing users to query the table while sensitive column values are obfuscated or nullified. For a deeper look at Delta Lake governance features, see our Delta Lake Interview Questions and Exam Prep Guide.

Question 25. Which DLT expectation syntax marks a row as failed and drops it from the output table when the constraint is violated?

• A) @dlt.expect("name", condition)
• B) @dlt.expect_or_warn("name", condition)
• C) @dlt.expect_or_drop("name", condition)
• D) @dlt.expect_or_fail("name", condition)

Answer: C - @dlt.expect_or_drop. DLT offers three expectation modes: expect records violations but keeps rows, expect_or_drop removes violating rows, and expect_or_fail halts the pipeline on any violation. Understanding these three behaviors is frequently tested.

Domain-by-Domain Breakdown and Study Tips

Now that you've worked through the questions, here's how to prioritize your remaining study time based on the domain weights and what the questions above reveal about common exam themes.

How to Score Your Practice Session

Tally your correct answers from the 25 questions above and use this guide to assess your readiness:

Score	Correct Answers	Readiness Assessment
90-100%	23-25	Exam ready - schedule your test this week
75-88%	19-22	Nearly there - review missed domains and take another full practice test
60-72%	15-18	More study needed - focus on your weakest two domains
Below 60%	0-14	Return to fundamentals - use a structured study guide before testing again

Remember, the actual exam requires 70% (about 32/45 questions). These 25 questions are a representative sample, not the full exam. If you scored 75%+ here, you're on a strong track - but keep practicing with full-length mock exams before the real thing. For a comprehensive set of questions that mirrors the full exam experience, visit our Databricks practice test platform for additional free and premium question sets.

Next Steps After Completing Practice Questions

Practice questions are most valuable when they're part of a structured study plan. Here's a recommended sequence for the final two to three weeks before your exam.

Week 1: Identify and Plug Knowledge Gaps

Use your score from these 25 questions to identify which domains need the most work. If you missed questions in Domain 3 (Data Processing), spend time in the Databricks documentation on Spark SQL and DataFrame APIs. If DLT questions tripped you up, build a simple DLT pipeline in a Databricks Community Edition account to get hands-on experience.

Week 2: Full-Length Practice Exams

Simulate real exam conditions: 45 questions, 90 minutes, no notes. Review every incorrect answer with the official Databricks documentation. Pay attention to how questions are worded - the exam often includes distractors that are partially correct.

Week 3: Targeted Review and Confidence Building

In the final week, focus on weak spots identified from your full-length practice tests. Re-read the exam guide from Databricks to ensure nothing has changed in the July 2025 version. For tips on passing without taking the expensive official training course, see our guide on Databricks Exam Tips: How to Pass Without the Official Course.

Also, if you're weighing this certification against others in the data space, our article on Databricks vs Snowflake Certification: Which Should You Get First? provides a practical framework for making that decision based on your career goals and current tech stack.

Finally, don't overlook the cost and logistics side of exam planning. Understanding retake policies, voucher discounts, and renewal requirements can save you money. Read our full breakdown at Databricks Certification Cost and Renewal: What You Need to Know.

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